Hysteresis modelling of reinforced concrete columns under pure cyclic torsional loading

Tarutal Ghosh Mondal,Sriharsha R. Kothamuthyala,S. Suriya Prakash 국제구조공학회 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.64 No.1

It has been observed in the past that, the reinforced concrete (RC) bridge columns are very often subjected to torsional moment in addition to flexure and shear during seismic vibration. Ignoring torsion in the design can trigger unexpected shear failure of the columns (Farhey et al. 1993). Performance based seismic design is a popular design philosophy which calls for accurate prediction of the hysteresis behavior of structural elements to ensure safe and economical design under earthquake loading. However, very few investigations in the past focused on the development of analytical models to accurately predict the response of RC members under cyclic torsion. Previously developed hysteresis models are not readily applicable for torsional loading owing to significant pinching and stiffness degradation associated with torsion (Wang et al. 2014). The present study proposes an improved polygonal hysteresis model which can accurately predict the hysteretic behavior of RC circular and square columns under torsion. The primary curve is obtained from mechanics based softened truss model for torsion. The proposed model is validated with test data of two circular and two square columns. A good correlation is observed between the predicted and measured torque-twist behavior and dissipated energy.

Autonomous vision-based damage chronology for spatiotemporal condition assessment of civil infrastructure using unmanned aerial vehicle

Tarutal Ghosh Mondal,Mohammad R. Jahanshahi 국제구조공학회 2020 Smart Structures and Systems, An International Jou Vol.25 No.6

This study presents a computer vision-based approach for representing time evolution of structural damages leveraging a database of inspection images. Spatially incoherent but temporally sorted archival images captured by robotic cameras are exploited to represent the damage evolution over a long period of time. An access to a sequence of time-stamped inspection data recording the damage growth dynamics is premised to this end. Identification of a structural defect in the most recent inspection data set triggers an exhaustive search into the images collected during the previous inspections looking for correspondences based on spatial proximity. This is followed by a view synthesis from multiple candidate images resulting in a single reconstruction for each inspection round. Cracks on concrete surface are used as a case study to demonstrate the feasibility of this approach. Once the chronology is established, the damage severity is quantified at various levels of time scale documenting its progression through time. The proposed scheme enables the prediction of damage severity at a future point in time providing a scope for preemptive measures against imminent structural failure. On the whole, it is believed that the present study will immensely benefit the structural inspectors by introducing the time dimension into the autonomous condition assessment pipeline.

A hybrid deep neural network compression approach enabling edge intelligence for data anomaly detection in smart structural health monitoring systems

Yuguang Fu,Tarutal Ghosh Mondal,Jau-Yu Chou,Jian-Xiao Mao 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.32 No.3

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

Deep learning-based post-disaster building inspection with channel-wise attention and semi-supervised learning

Rih-Teng Wu,Wen Tang,Tarutal Ghosh Mondal,Abhishek Subedi,Mohammad R. Jahanshahi 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.31 No.4

The existing vision-based techniques for inspection and condition assessment of civil infrastructure are mostly manual and consequently time-consuming, expensive, subjective, and risky. As a viable alternative, researchers in the past resorted to deep learning-based autonomous damage detection algorithms for expedited post-disaster reconnaissance of structures. Although a number of automatic damage detection algorithms have been proposed, the scarcity of labeled training data remains a major concern. To address this issue, this study proposed a semi-supervised learning (SSL) framework based on consistency regularization and cross-supervision. Image data from post-earthquake reconnaissance, that contains cracks, spalling, and exposed rebars are used to evaluate the proposed solution. Experiments are carried out under different data partition protocols, and it is shown that the proposed SSL method can make use of unlabeled images to enhance the segmentation performance when limited amount of ground truth labels are provided. This study also proposes DeepLab-AASPP and modified versions of U-Net++ based on channel-wise attention mechanism to better segment the components and damage areas from images of reinforced concrete buildings. The channel-wise attention mechanism can effectively improve the performance of the network by dynamically scaling the feature maps so that the networks can focus on more informative feature maps in the concatenation layer. The proposed DeepLab-AASPP achieves the best performance on component segmentation and damage state segmentation tasks with mIoU scores of 0.9850 and 0.7032, respectively. For crack, spalling, and rebar segmentation tasks, modified U-Net++ obtains the best performance with Igou scores (excluding the background pixels) of 0.5449, 0.9375, and 0.5018, respectively. The proposed architectures win the second place in IC-SHM2021 competition in all five tasks of Project 2.

Ensemble-based deep learning for autonomous bridge component and damage segmentation leveraging Nested Reg-UNet

Rih-Teng Wu,Abhishek Subedi,Wen Tang,Tarutal Ghosh Mondal,Mohammad R. Jahanshahi 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.31 No.4

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.